Computerised Guidelines Implementation: Obtaining Feedback for Revision of Guidelines, Clinical Data Model and Data Flow

2007 ◽  
pp. 461-466 ◽  
Author(s):  
S. Panzarasa ◽  
S. Quaglini ◽  
A. Cavallini ◽  
S. Marcheselli ◽  
M. Stefanelli ◽  
...  
Keyword(s):  
Clinical Data ◽  
Data Model ◽  
Data Flow ◽  
Guidelines Implementation ◽  
Clinical Data Model

scholarly journals A nursing clinical data model for neuromuscular processes: content analysis of the Portuguese nursing customization

2019 ◽  
Vol 24 (5) ◽  
pp. 1609-1616
Author(s):  
Hugo Neves ◽  
Paulo Parente
Keyword(s):  
Content Analysis ◽  
Clinical Data ◽  
Data Model ◽  
Nursing Practice ◽  
Clinical Findings ◽  
Negative Judgment ◽  
Definition Of ◽  
Nursing Clinical ◽  
Clinical Data Model

Abstract This study targets the development of a nursing clinical data model for neuromuscular processes. To achieve this purpose, content analysis based on Bardin’s perspective was performed on the Portuguese nursing local customizations regarding neuromuscular processes, with the International Classification for Nursing Practice concepts and the ISO 18104:2014 used as encoding rules. From analysis of the data, a total of 1766 diagnoses were related to neuromuscular processes. After application of exclusion criteria, a corpus with a total of 900 diagnoses was subjected to content analysis. After application of the encoding rules, a total of 81 context units were obtained, and through an inductive approach, were defined into three categories: clinical findings (e.g. aphasia); negative judgment diagnoses (e.g. impaired communication); transition properties (e.g. preparation and knowledge). These interpretations were validated by experts in the field. This study not only demonstrates the need to standardize data, but also the importance of neuromuscular processes in nursing practice. We hope this study will guide the definition of a nursing clinical data model that will help in increasing complexity in the level of care provided with high impact in the patient’s quality of life.

scholarly journals A novel tool for standardizing clinical data in a realism-based common data model

2020 ◽  
Author(s):  
Hayden G. Freedman ◽  
Heather Williams ◽  
Mark A. Miller ◽  
David Birtwell ◽  
Danielle L. Mowery ◽  
...  
Keyword(s):  
Clinical Data ◽  
Data Model ◽  
Clinical Information ◽  
Clinical Situation ◽  
Common Data Model ◽  
Full Potential ◽  
Semantic Web Technologies ◽  
High Quality Research ◽  
Rdf Data ◽  
Clinical Data Model

AbstractStandardizing clinical information in a common data model is important for promoting interoperability and facilitating high quality research. Semantic Web technologies such as Resource Description Framework can be utilized to their full potential when a clinical data model accurately reflects the reality of the clinical situation it describes. To this end, the Open Biomedical Ontologies Foundry provides a set of ontologies that conform to the principles of realism and can be used to create a realism-based clinical data model. However, the challenge of programmatically defining such a model and loading data from disparate sources into the model has not been addressed by pre-existing software solutions. The PennTURBO Semantic Engine is a tool developed at the University of Pennsylvania that works in conjunction with data aggregation software to transform source-specific RDF data into a source-independent, realism-based data model. This system sources classes from an application ontology and specifically defines how instances of those classes may relate to each other. Additionally, the system defines and executes RDF data transformations by launching dynamically generated SPARQL update statements. The Semantic Engine was designed as a generalizable RDF data standardization tool, and is able to work with various data models and incoming data sources. Its human-readable configuration files can easily be shared between institutions, providing the basis for collaboration on a standard realism-based clinical data model.

Linking a Medical Vocabulary to a Clinical Data Model using Abstract Syntax Notation 1

1998 ◽  
Vol 37 (04/05) ◽  
pp. 440-452 ◽  
Author(s):  
R. A. Rocha ◽  
H. R. Solbrig ◽  
M. W. Barnes ◽  
S. P. Schrank ◽  
M. Smith ◽  
...  
Keyword(s):  
Clinical Data ◽  
Data Model ◽  
Clinical Information System ◽  
Clinical Information ◽  
Abstract Syntax ◽  
Data Types ◽  
Clinical Model ◽  
Semantic Relationships ◽  
Important Intermediate ◽  
Clinical Data Model

AbstractWe have created a clinical data model using Abstract Syntax Notation 1 (ASN.l). The clinical model is constructed from a small number of simple data types that are built into data structures of progressively greater complexity. Important intermediate types include Attributes, Observations, and Events. The highest level elements in the model are messages that are used for inter-process communication within a clinical information system. Vocabulary is incorporated into the model using BaseCoded, a primitive data type that allows vocabulary concepts and semantic relationships to be referenced using standard ASN.l notation. ASN.l subtyping language was useful in preventing unbounded proliferation of object classes in the model, and in general, ASN.l was found to be a flexible and robust notation for representing a model of clinical information.

scholarly journals Nursing Process Addressing the Nursing Focus “Hallucination”: A Scoping Review

2019 ◽  
pp. 105477381987753
Author(s):  
Patrícia Daniela Barata Gonçalves ◽  
Francisco Miguel Correia Sampaio ◽  
Carlos Alberto da Cruz Sequeira ◽  
Maria Antónia Taveira da Cruz Paiva e Silva
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Clinical Data ◽  
Nursing Care ◽  
Data Model ◽  
Care Quality ◽  
Nursing Process ◽  
Scoping Reviews ◽  
Nursing Care Quality ◽  
Clinical Data Model

Although hallucinations are prevalent in psychiatric disorders, such as psychosis or dementia, no studies were to be found in literature about the nursing process addressing the focus “Hallucination”. This literature review, which is integrated with a scoping study framework, was performed to determine a clinical data model addressing the focus “Hallucination”. PRISMA checklist for scoping reviews was followed. From the total of 328 papers found, 32 were selected. The findings of this review were summarized according to the nursing process addressing the focus “Hallucination”. These findings led to determine a clinical data model addressing the focus “Hallucination”, comprising the elements of the nursing process. This clinical data model may contribute toward improving nursing decision-making and nursing care quality in relation to a client suffering from hallucination, as well as contribute toward producing more reliable nursing-sensitive indicators.

Harmonization of clinical data across Gen3 data commons.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18094-e18094 ◽  
Author(s):  
LaRon Hughes ◽  
Robert L. Grossman ◽  
Zachary Flamig ◽  
Andrew Prokhorenkov ◽  
Michael Lukowski ◽  
...  
Keyword(s):  
Open Source ◽  
Clinical Data ◽  
Open Source Software ◽  
Data Model ◽  
Reference Standards ◽  
Data Dictionary ◽  
Software Platform ◽  
Operating Data ◽  
External Reference ◽  
Data Commons

e18094 Background: Gen3 is an open source software platform for developing and operating data commons. Gen3 systems are now used by a variety of institutions and agencies to share and analyze large biomedical datasets including clinical and genomic data. One of the challenges of working with these datasets is disparate clinical data standards used by researchers across different studies and fields. We have worked to address these hurdles in a variety of ways. Methods: Gen3 is an open source software platform for developing and operating data commons. Detailed specification and features can be found at https://gen3.org/ with code located on GitHub ( https://github.com/UC-cdis ). Results: The Gen3 data model is a graphical representation of the different nodes or classes of data that have been collected. Examples include diagnosis, demographic, exposure, and family history. The properties and values on each node are controlled by the data dictionary specified by the data commons creator. While each commons may have a unique data model and dictionary, specifying external standards allows for easier submission of new data and assists data consumers with interpretation of results. A variety of external references can be supported, but here we demonstrate the use of the National Cancer Institute Thesaurus (NCIt). NCIt provides reference terminologies and biomedical standards that contain a rich set of terms, codes, definitions, and concepts. Using the same reference standards across commons allows for the export of clinical data between commons. The Portable Format for Biomedical Data (PFB) was created to facilitate data export and to allow the data dictionary schema as well as the raw data to be compressed and exported. This new file format, which utilizes an Avro serialization, is small, fast, easy to modify, and enables simple data export and import. PFB also has the ability to house entire external reference ontologies and it is easy to update the PFB references as changes are introduced. Conclusions: We have shown here how the Gen3 data model, use of external reference standards for clinical data, and the export/import format of PFB enable the harmonization of clinical data across different data commons.

A Study on Data Flow Model of Web to Print

2011 ◽  
Vol 201-203 ◽  
pp. 1412-1415
Author(s):  
Zhen Liu ◽  
Chang Xin Liu ◽  
Sheng Wei Yang
Keyword(s):  
Data Model ◽  
Flow Model ◽  
Data Flow ◽  
Flow Diagram ◽  
Analysis Method ◽  
Uniform Model ◽  
Data Flow Diagram ◽  
Business Flow ◽  
Data Flow Model ◽  
Analyze Data

As to the complexity data transmission and processing of web to print（W2P） system, The input and output of these data are analyzed and classified in this paper, based on the actual demand of ShangHai TongKun Digital Printing Enterprise, structure analysis method are used to construct the data model of W2P as a form of Data Flow Diagram(DFD), and then the single order’s business flow model are built with Uniform Model Language(UML). At last, both the two models are combined to illustrate the corresponding relationship in the data exchanges. This study is helpful to analyze data of W2P system and can decrease risk during software’s development.

scholarly journals PERANCANGAN SISTEM INFORMASI ELEKTRONIK LOG BOOK PENANGKAPAN IKAN BERBASIS WEB

2016 ◽  
Vol 11 (1) ◽  
pp. 53
Author(s):  
Hadhi Nugroho ◽  
Adi Darmawan ◽  
Agus Sufyan
Keyword(s):  
Real Time ◽  
Data Model ◽  
Input Data ◽  
Data Flow ◽  
Flow Diagram ◽  
Physical Data ◽  
Data Flow Diagram ◽  
Level 1 ◽  
Conceptual Data ◽  
Level 2

Pusat Pengkajian dan Perekayasaan Teknologi Kelautan dan Perikanan (P3TKP) - Kementerian Kelautan dan Perikanan (KKP) telah mengembangkan teknologi elektronik log book penangkapan ikan berbasis GPRS. Elektronik log book merupakan perangkat keras yang memiliki fungsi utama untuk input data tangkapan ikan secara elektronik. Untuk menampilkan data elektronik log book menjadi informasi statistik secara real time, diperlukan sebuah sistem informasi berbasis web. Tahapan perancangan sistem informasi elektronik log book penangkapan ikan berbasis web terdiri dari identifikasi kebutuhan sistem, perancangan perangkat lunak, dan implementasi. Perancangan desain sistem database elektronik log book menggunakan MySQL. Desain sistem database tersebut berisi Conceptual Data Model (CDM) dan Physical Data Model (PDM). Untuk menggambarkan interaksi pengguna dengan sistem informasi elektronik log book dapat dilihat pada Data Flow Diagram (DFD). DFD pada perancangan sistem informasi elektronik log book terdiri dari DFD level-0, DFD level-1, dan DFD level-2. Modul-modul pada sistem informasi elektronik log book terdiri dari modul autentifikasi, modul data tangkapan, modul informasi harga ikan, dan modul informasi cuaca. Aplikasi kemudian diimplementasikan di domain www.p3tkp-elogbook.net. Sistem informasi elektronik log book penangkapan ikan berbasis web yang diaplikasikan ini dapat memberikan informasi secara cepat dan tepat tentang data penangkapan ikan, informasi harga ikan, dan informasi cuaca perairan, dengan kerahasiaan data yang terjamin.

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